I have a csv with every row as a document. I need to perform LDA upon this. I have the following code :
library(tm)
library(SnowballC)
library(topicmodels)
library(RWeka)
X = read.csv('doc.csv',sep=",",quote="\"",stringsAsFactors=FALSE)
corpus <- Corpus(VectorSource(X))
corpus <- tm_map(tm_map(tm_map(corpus, stripWhitespace), tolower), stemDocument)
corpus <- tm_map(corpus, PlainTextDocument)
BigramTokenizer <- function(x) NGramTokenizer(x, Weka_control(min = 2, max = 2))
dtm <- DocumentTermMatrix(corpus, control = list(tokenize=BigramTokenizer,weighting=weightTfIdf))
At this point checking the dtm object gives
<<DocumentTermMatrix (documents: 52, terms: 477)>>
Non-/sparse entries: 492/24312
Sparsity : 98%
Maximal term length: 20
Weighting : term frequency - inverse document frequency (normalized) (tf-idf)
Now I proceed to perform LDA upon this
rowTotals <- apply(dtm , 1, sum)
dtm.new <- dtm[rowTotals> 0, ]
g = LDA(dtm.new,10,method = 'VEM',control=NULL,model=NULL)
I get the following error
Error in LDA(dtm.new, 10, method = "VEM", control = NULL, model = NULL) :
The DocumentTermMatrix needs to have a term frequency weighting
The Document Term matrix was clearly weighted. What am I doing wrong ?
Kindly Help.
The Document Term matrix needs to have a term frequency weighting: